Tasks Key Responsibilities Data Engineering & Platform Architecture Architect, design, and govern scalable batch and streaming data platforms using Python (strong OOP design), Apache Spark (PySpark, Spark SQL), and Azure Databricks. Define end-to-end reference architectures for real-time and near-real-time streaming solutions using Apache Kafka or Apache Flink, with Spark Structured Streaming or Apache Flink, ensuring: Event-time correctness Exactly-once processing guarantees Stateful stream processing and checkpointing High availability and fault tolerance Own the architectural design of Medallion architecture (Bronze, Silver, Gold) on ADLS Gen2 with Delta Lake, including data lifecycle, retention, and cost optimization strategies. Lead data governance architecture, defining standards for security, access control, lineage, and metadata using Databricks Unity Catalog. Design domain-oriented, scalable data products aligned with Data Mesh and cloud-native architecture principles. Define integration patterns for data services, streaming producers/consumers, and microservices-based architectures. Architect and oversee deployment of data and streaming workloads on AKS, including container strategy, scaling, resiliency, and networking. Define and enforce performance, scalability, and reliability standards for Spark and streaming workloads (partitioning, Z-ordering, state tuning, caching). Establish data quality, validation, and schema evolution standards for both batch and streaming pipelines. Design secure-by-default architectures using Azure IAM, RBAC, Key Vault, private endpoints, VNet integration, and network isolation. Lead CI/CD and DevOps architecture using GitLab, enabling automated testing, deployment, rollback, and environment promotion. Define Infrastructure as Code architecture using Terraform and Pulumi for repeatable, auditable deployments. Establish observability architecture (monitoring, logging, alerting) across Databricks, AKS, streaming platforms, and Azure services. Review and approve solution designs, ADRs, and technical proposals, ensuring alignment with enterprise standards. Product Owner (PO) Responsibilities Act as Technical Product Owner for data platforms and streaming solutions, owning one or more data domains or products. Partner with business stakeholders, analysts, and architects to translate business objectives into architecture-aligned epics, user stories, and acceptance criteria. Own and prioritize the product and technical backlog, balancing feature delivery, technical debt, scalability, security, and cost efficiency. Define and track product KPIs (data latency, data quality, availability, adoption, platform cost). Drive roadmap definition for data and streaming platforms, aligned with enterprise strategy and architectural vision. Manage dependencies, risks, and cross-team coordination across engineering, analytics, DevOps, and security teams. Support release planning, stakeholder communication, and architectural decision-making. Act as a subject-matter expert and decision authority for the data platform. Leadership & Engineering Excellence Provide architectural and technical leadership to data engineers and cross-functional teams. Conduct design reviews, code reviews, and architecture walkthroughs. Mentor engineers on distributed systems, streaming design, cloud-native patterns, and performance optimization. Establish and enforce coding standards, design patterns, and best practices. Champion continuous improvement, innovation, and engineering excellence. Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 8–12 years of experience in Data Engineering, with demonstrated architecture ownership on Azure-based platforms. Strong proficiency in Python, with solid OOP, design patterns, and system design principles. Deep expertise in Apache Spark (PySpark, Spark SQL) and Azure Databricks. Strong hands-on and architectural experience with streaming platforms: Apache Kafka OR Apache Flink Spark Structured Streaming Proven experience designing microservices and event-driven architectures. Strong experience deploying and operating workloads on Azure Kubernetes Service (AKS). Deep understanding of Delta Lake and large-scale lakehouse architectures. Advanced SQL skills for analytics and optimization. Strong experience with Azure ADLS Gen2, Databricks, Azure Functions, Service Bus, Key Vault. Strong experience with Git, GitLab CI/CD, and release management. Experience with Terraform and Pulumi for enterprise-grade IaC. Strong knowledge of data modeling, distributed systems, and fault tolerance Nice to Have Experience with both Kafka and Flink in large-scale production systems. Exposure to Kafka Schema Registry, CDC, and event versioning. Experience implementing Data Mesh or domain-driven data platforms. Exposure to DevSecOps and Zero Trust architectures. Experience with Generative AI / LLM-enabled data platforms (RAG, embeddings, vector databases). Background in regulated or large enterprise environments (banking, automotive, telecom).

About Mercedes-Benz India

Mercedes-Benz India Pvt. Ltd. was established in 1994 at Pune, India with the aim to become the fastest green-field operations ever to be created. We are currently rated as one of the top CKD plants of Mercedes-Benz globally We’re pioneers in delivering world-class automobile solutions built with intelligent technology and the will to create ‘The Best or Nothing’. With our unwavering set of values and the continuous pursuit of excellence through the years, we work towards delivering a sustainable future that offers unparalleled luxury and premium brand experience through our products and services. Our product portfolio ranges from the New Generation Cars, to the luxury sedans like the Masterpiece of Intelligence – The E-Class, the Essence of Luxury - Mercedes-Maybach; to the adventurous SUVs like the GLC and the GLS; along with the performance-driven AMGs.